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Ultra-short baseline (USBL) underwater positioning systems are widely used in marine scientific research and ocean engineering. Angle misalignment is a main error that reduces the accuracy of USBL underwater positioning. The conventional angle-calibration method assumes that the transponder position obtained by USBL positioning is an errorless coefficient matrix. However, errors inevitably exist in the estimation of the transponder's position via USBL positioning, and the precision varies at different epochs. Ignoring the error in the transponder's position will significantly reduce the precision of the angle misalignment estimation. In this paper, a new angle-calibration method is proposed for precise USBL underwater positioning. The angle alignment model is derived by treating the transponder's position obtained by USBL positioning as an observation, and the stochastic model is then established according to the bearing angles. Robust estimation is likewise applied to further improve the precision of the angle misalignment estimation. To verify the performance of the proposed method, a sea experiment was performed. The results show that the new method has high calibration accuracy and robustness. The estimation precision of this method is improved by 0.0457°~0.6896° in heading, 0.0125°~0.8072° in roll, and 0.0077°~0.9436° in pitch, compared with that of the conventional angle alignment method. [ABSTRACT FROM AUTHOR]

Ultra-short baseline (USBL) underwater positioning systems are widely used in marine scientific research and ocean engineering. Angle misalignment is a main error that reduces the accuracy of USBL underwater positioning. The conventional angle-calibration method assumes that the transponder position obtained by USBL positioning is an errorless coefficient matrix. However, errors inevitably exist in the estimation of the transponder’s position via USBL positioning, and the precision varies at different epochs. Ignoring the error in the transponder’s position will significantly reduce the precision of the angle misalignment estimation. In this paper, a new angle-calibration method is proposed for precise USBL underwater positioning. The angle alignment model is derived by treating the transponder’s position obtained by USBL positioning as an observation, and the stochastic model is then established according to the bearing angles. Robust estimation is likewise applied to further improve the precision of the angle misalignment estimation. To verify the performance of the proposed method, a sea experiment was performed. The results show that the new method has high calibration accuracy and robustness. The estimation precision of this method is improved by 0.0457°~0.6896° in heading, 0.0125°~0.8072° in roll, and 0.0077°~0.9436° in pitch, compared with that of the conventional angle alignment method.

This paper firstly focuses on the automatic detection of mental health in colleges and universities and the sequential quadratic programming algorithm, proposes a psychological abnormality detection model for college students based on the sequential quadratic programming algorithm, and in the feature vectorization process, uses constraint functions and coefficient matrices to process various types of features into vector sequences of length k weeks to help quantify the trajectory of students’ school activities, and trains and establishes a student psychological abnormality detection model. Then, on the technical basis of the psychological abnormality detection model, the mental health education system is constructed, the online and offline integration teaching development is realized through the teaching system, and the performance of the education system is verified. The results show that the average score of the traditional psychological teaching model 1-10 week test is 76.52, compared to the average score of 78.51 in the mental health education system teaching 1-10 test, with an error value of 1.89. This study uses the mental health teaching system to retrieve information on students with current mental health problems at any time, and there this is to realize dynamic online and offline coeducation.

In this paper, the 15 original variables of the Airpak-based influence factor scale for art and design talent training were first determined, and the five-level scoring method was applied to score the 15 influence factors. Then, we used factor analysis to factorize the data, standardize the sample data, construct a coordinate system with common factors, and use the projection of each variable in the coordinate system instead of the original variables to construct the correlation coefficient matrix R. Then, we solved the correlation coefficient matrix between each variable, selected the number of common factors, and calculated the eigenvectors of the factor loading matrix A to construct the influence factor analysis model. Finally, the model is used to analyze the weight of each influencing factor of Airpak-based art and design talent cultivation and to build an innovative path for talent cultivation. In terms of students’ own factors, the influence weight of learning enthusiasm on talent cultivation is 0.48, and the influence weights of learning achievement and design talent are 0.43 and 0.545, respectively; in terms of teaching level factors, the influence weights of teachers’ teaching ability, professional level and design experience on talent cultivation are 0.44, 0.5 and 0.37 respectively. The research of this paper has an important reference and reference value for the cultivation of art and design talents.

Context: The accuracy of estimated breeding values (EBVs) is an important metric in genetic evaluation systems in Australia. With reduced costs for DNA genotyping due to advances in molecular technology, more and more animals have been genotyped for EBVs. The rapid increase in genotyped animals has grown beyond the capacity of the current genomic best linear unbiased prediction (GBLUP) method. Aims: This study aimed to implement and evaluate a new single-nucleotide polymorphism (SNP)–BLUP model for the computation of prediction error variances (PEVs) to accommodate the increasing number of genotyped animals in beef and sheep single-step genetic evaluations in Australia. Methods: First, the equivalence of PEV estimates obtained from both GBLUP and SNP-BLUP models was demonstrated. Second, the computing resources required by each model were compared. Third, within the SNP-BLUP model, the PEVs obtained from subsets of SNP were evaluated against those from the complete dataset. Fourth, the new model was tested in the Australian Merino sheep and Angus beef cattle datasets. Key results: The PEVs of genotyped animals calculated from the SNP–BLUP model were equivalent to the PEVs derived from the GBLUP model. The SNP–BLUP model used much less time than did the GBLUP model when the number of genotyped animals was larger than the number of SNPs. Within the SNP–BLUP model, the running time could be further reduced using a subset of SNPs makers, with high correlations (>0.97) observed between the PEVs obtained from the complete dataset and subsets. However, it is important to exercise caution when selecting the size of the subsets in the SNP–BLUP model, as reducing the subset size may result in an increase in the bias of the PEVs. Conclusions: The new SNP-BLUP model for PEV calculation for genotyped animals outperforms the current GBLUP model. A new accuracy program has been developed for the Australian genetic evaluation system which uses much less memory and time to compute accuracies. Implications: The new model has been implemented in routine sheep and beef genetic evaluation systems in Australia. This development ensures that the calculation of accuracies is sustainable, with increasing numbers of animals with genotypes. The accuracy of estimated breeding values is routinely provided to breeders as part of genetic evaluations in Australia. A new method to approximate accuracies using genomic information has been developed and is presented in this paper. The new model has been implemented in Australia's routine sheep and beef genetic evaluation systems. This development ensures that the calculation of accuracies is sustainable with increasing numbers of animals with genotypes. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spectral-type subspace clustering algorithms have attracted wide attention because of their excellent performance displayed in a great deal of applications in machine learning domain. It is critical for spectral-type subspace clustering algorithms to obtain suitable coefficient matrices which could reflect the subspace structures of data sets. In this paper, we propose a latent block diagonal representation clustering algorithm (LBDR). For a data set, the goal of LBDR is to construct a block diagonal and dense coefficient matrix and settle the noise adaptively within the original data set by using dimension reduction technique concurrently. In brief, by seeking the solution of a joint optimization problem, LBDR is capable of finding a suitable coefficient matrix and a projection matrix. Furthermore, a series of experiments conducted on several benchmark databases show that LBDR dominates the related methods. [ABSTRACT FROM AUTHOR]

When processing sensing signals under low signal-to-noise ratio environment, the sensing performance cannot be guaranteed in existing algorithms. To ensure sensing performance, we propose a novel spectrum sensing algorithm based on soft low-rank subspace clustering (SLRSC) in this brief. Firstly, the lowest rank coefficient matrix of signal vectors is calculated by low-rank representation, and the adjacency matrix is built to make coefficient matrix balance. Secondly, the eigenvalue of the adjacency covariance matrix is extracted as a feature. Finally, variable weighting $k$ -means method is used to cluster, which avoids complicated threshold derivation and improves cluster accuracy. Simulation results prove that the proposed SLRSC algorithm has excellent sensing performance under high noise case. [ABSTRACT FROM AUTHOR]

The existing graph-based RGBT tracking methods mainly focus on assigning a weight to each local image patch to suppress background influence in target bounding box, but the influences of background clutter might limit the improvement of tracking performance. To solve this problem, we propose a new algorithm, called cooperative low-rank graph model, to suppress background clutter. Specifically, the proposed feature decomposition module decomposes input dual-modal features into low-rank components and sparse noisy components, which could be used collaboratively by regularizing graph learning by combining modal weights. Besides, to avoid SVD (Singular Value Decomposition) operations we have designed an efficient solver based on ADMM (Alternating Direction Methods of Multipliers), which could factorize the low-rank matrix into two low-dimensional submatrices. Extensive experiments on four RGBT tracking benchmark data sets show that our method performs favorably against other state-of-the-art tracking algorithms, and achieves more robust tracking performance. [ABSTRACT FROM AUTHOR]

In coal mine engineering, numerical software is used to analyze the behavior of coal rock damage and fluid migration. The order of the coefficient matrix used in numerical calculations is increasing, and this increases the computation steps in obtaining the coefficient matrix solution. The storage and solution of the coefficient matrix are key factors influencing the efficiency of the numerical software. Therefore, to save storage space and reduce the computation steps, the coefficient matrix must be effectively compressed and stored. In this work, the structural characteristics of different coefficient matrices are analyzed in detail, and we find that for different computational regions, as long as the nodes are numbered according to certain rules, the corresponding coefficient matrices will have similar structural characteristics. The nonzero elements are symmetrically distributed in the diagonal band, and all the elements on both sides outside the band are zero. Based on this, the coefficient matrix is compressed by a pivoting scheme, and the compressed matrix is directly eliminated by dislocation Gaussian elimination. Thus, a compressed storage method that integrates the compression and solution of the coefficient matrix is established. The compressed storage and calculation module is incorporated into our self-developed simulation software COMBUSS-3D to simulate the evolution of the temperature field in the goaf of Luling Coal Mine. Compared with the conventional method, the compressed storage module can significantly improve the computing rate of the simulation, by approximately 80%. [ABSTRACT FROM AUTHOR]

Image denoising is a crucial step in order to improve digital image quality. Furthermore, the digital image in sparse format especially in low-rank structure has been utilized in several multimedia applications. Non-local similarity algorithm is used to increase the level of noise removal methods and improve the image visual quality due to the pivotal correlation in the image inter-patches, and intra-correlation with a low-rank prior of the texture itself where the second generation wavelet is exploited to develop a similar coefficients to the image matrix. In order to solve the burden of selected contaminated pixels in the sub-patches, the singular value shrinkage is used to guarantee the elimination of high noisy pixels. Furthermore, the proposed method uses random matrix in order to practically choose the level of singular value threshold. As the experimental results depicts, the proposed algorithm has superior performance in peak signal to noise ratio (PSNR), structural similarity index (SSIM), image quality assessment (IQA) and figure of merit (FOM) in comparison with state of the art noise removal techniques and can recover better details and less artifacts and blurring. In addition, to show the quality of complexity time of the proposed method, execution time of the proposed method and several denoising methods has been examined. [ABSTRACT FROM AUTHOR]

Advances in information technology have harnessed the application of Quick Response (QR) codes in day-to-day activities, simplifying information exchange. QR codes are witnessed almost everywhere, on consumables, newspapers, information bulletins, etc. The simplicity of QR code creation and ease of scanning with free software have tremendously influenced their wide usage, and since QR codes place information on an object they are a tool for the IoT. Many healthcare IoT applications are deployed with QR codes for data-labeling and quick transfer of clinical data for rapid diagnosis. However, these codes can be duplicated and tampered with easily, attributed to open- source QR code generators and scanners. This paper presents a novel (n,n) secret-sharing scheme based on Nonnegative Matrix Factorization (NMF) for secured transfer of QR codes as multiple shares and their reconstruction with a regularized Super Resolution Convolutional Neural Network (SRCNN). This scheme is an alternative to the existing polynomial and visual cryptography-based schemes, exploiting NMF in part-based data representation and structural regularized SRCNN to capture the structural elements of the QR code in the super-resolved image. The experimental results and theoretical analyses show that the proposed method is a potential solution for secured exchange of QR codes with different error correction levels. The security of the proposed approach is evaluated with the difficulty in launching security attacks to recover and decode the secret QR code. The experimental results show that an adversary must try 258 additional combinations of shares and perform 3 × 288 additional computations, compared to a representative approach, to compromise the proposed system. [ABSTRACT FROM AUTHOR]

Aiming at the poor prediction performance of the traditional GM(1,N) model, this paper proposes the GM(1,N) model with expression optimization: firstly, unknown parameters are introduced into the coefficient matrix of the traditional GM(1,N) model to obtain the model expression with unknown parameters: Then the average relative error function with unknown parameters is constructed: Finally, the particle swarm optimization algorithm is used to obtain the parameter column with the smallest average relative error. Taking the carbon emission of the logistics industry in Hubei Province as an example, this paper forecasts the carbon emission by using the traditional GM(1,N) model, GM(1, N) model with background value optimization, and GM(1, N) model with expression optimization. By comparing and analyzing the prediction results of the three models, it is concluded that GM(1,N) model with expression optimization has better prediction performance. [ABSTRACT FROM AUTHOR]

To improve the ability of the acoustic computed tomography (CT) temperature field reconstruction system for reconstructing various temperature distributions in the measured area, square and circular layouts of transducers were studied. In case of the square layout, the focus was on the placement of the transducers, and in case of the circular layout, the focus was on the division of grids. We proposed the coefficient matrix theory and indexes for studying and evaluating transducers layout. Four typical temperature field models were simulated and reconstructed. Simulation results showed that in a square layout, optimizing the position of transducers can result in better reconstruction quality. In a circular layout, a regular hexagonal grid division was better than an equal-area division, and better reconstruction quality was obtained by choosing the appropriate number of grids. For a small number of transducers, the circular layout of nine transducers with 27 acoustic rays and 91 regular hexagonal grids resulted in better reconstruction quality than the square and circular layout of eight transducers; in each reconstruction, the error was ≤ 5.07%. [ABSTRACT FROM AUTHOR]

Surface location error (SLE) caused by the forced vibration is one of the main factors limiting machining accuracy in stable milling. This paper presents a novel scheme with high precision and high efficiency to predict SLE based on the Newton polynomial-Chebyshev nodes. The dynamic model embodying the forced and regenerative excitations is first derived from the simplified milling process. After describing the response of the system with the direct integration format, the Newton polynomial frame is chosen to spread the self-excited and forced-excited elements successively in each equidistant time interval to obtain the separate output of tool vibration displacements with a close form. Then, the coefficient matrix which is not affected by the change of time is established in each spindle speed location to calculate SLE. Three examples which have been verified by the experiments are introduced to capture the optimal approximated order with exhibiting the characteristic of the proposed methods. It is observed that the prediction accuracy of the high-order methods drop seriously for the absence of Runge effect. In order to avoid this unwanted phenomenon, the origin uniform time joints are modified by a nonlinear sampling technique based on Chebyshev nodes to establish the novel coefficient matrix with fewer calculated loads for SLE prediction, and a series of comparisons illuminate the proposed modified method performs satisfactorily. [ABSTRACT FROM AUTHOR]

The change of electrodes' conductivity is a crucial parameter during battery aging process, non-contact detection of battery electrodes' defects through conductivity reconstruction is an innovative technology. In this paper, the magnetic induction tomography (MIT) was applied to reconstruct the conductivity of electrodes, the simplified battery models with complete and broken electrodes were chosen as target A and target Brespectively, and an eight-channel MIT system was designed to measure the change of mutual induced voltage. A method of computing the forward problem called 'A·E' was adopted to compute the coefficient matrix for the forward problem and the frequency 12.665kHz was chosen as the excitation frequency. Based on the conductivity reconstruction images, relative errors of reconstructed conductivity and objective function, 'A·E' method exhibited advantages in terms of computation time and accuracy compared to coefficient sensitivity method with the simulation data. In order to test the accuracy of 'A·E' method, data measured by MIT system was applied in the inverse problem with four regularization parameter selection methods. L-curve criterion and generalized cross-validation criterion identified defects successfully as they were more sensitive to the change of conductivity. [ABSTRACT FROM AUTHOR]

In this article, a fast numerical approach is proposed for finding the solution of nonlinear delay differential equations by using hybrid Taylor and Block-pulse Functions (HTBPFs). Firstly, some features of hybrid functions which are a combination of Block-Pulse functions and Taylor polynomials on the interval are introduced [0; 1) . In this spectral approach, the operational matrices of stretch, derivation and coefficient matrices are utilized. Based on these piecewise functions, we transfer delay differential equations (DDEs) into a system of linear or nonlinear algebraic equations. Also, in this numerical approach, it is shown that these operational matrices are sparse which is an effective advantage of the fast implementation of numerical computation. Then, error analysis is done. Finally, three examples are solved to show that the new proposed approach is comparable with other methods of high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In coal mine engineering, numerical software is used to analyze the behavior of coal rock damage and fluid migration. The order of the coefficient matrix used in numerical calculations is increasing, and this increases the computation steps in obtaining the coefficient matrix solution. The storage and solution of the coefficient matrix are key factors influencing the efficiency of the numerical software. Therefore, to save storage space and reduce the computation steps, the coefficient matrix must be effectively compressed and stored. In this work, the structural characteristics of different coefficient matrices are analyzed in detail, and we find that for different computational regions, as long as the nodes are numbered according to certain rules, the corresponding coefficient matrices will have similar structural characteristics. The nonzero elements are symmetrically distributed in the diagonal band, and all the elements on both sides outside the band are zero. Based on this, the coefficient matrix is compressed by a pivoting scheme, and the compressed matrix is directly eliminated by dislocation Gaussian elimination. Thus, a compressed storage method that integrates the compression and solution of the coefficient matrix is established. The compressed storage and calculation module is incorporated into our self-developed simulation software COMBUSS-3D to simulate the evolution of the temperature field in the goaf of Luling Coal Mine. Compared with the conventional method, the compressed storage module can significantly improve the computing rate of the simulation, by approximately 80%.

The observation errors of independent variables in model prediction are typically neglected in traditional regression models, which leads to the decreased prediction accuracy. In this paper, a new regression prediction method that is based on the partial errors-in-variables (partial EIV) model is proposed, which takes into account the observation errors of all variables. It observes that each element of the coefficient matrix is an expression or a function. The partial EIV model is transformed into a linear model and constructed in the form of indirect adjustment for iterative solution. By considering the errors in the independent variables when predicting the corresponding dependent variables with the partial EIV model, the proposed method can achieve higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The coefficient matrix and local unitary (LU) transformation invariant of three-qubit states are studied in this paper. There is general relation between the coefficient matrix and independent LU transformation invariant existing which is shown. Especially, the three coefficient matrix can completely determine six algebraically independent local invariants for three-qubit pure states. [ABSTRACT FROM AUTHOR]

In order to calculate eddy current testing (ECT) signals due to a complex natural crack by using a regular mesh, the multi-media element (MME) was proposed by authors. In this paper, a new high-precision method for calculating the MME coefficient matrices is proposed and implemented in the finite-element method (FEM)–BEM hybrid code for ECT of narrow cracks. Through the comparison of the new MME solutions with those of the conventional FEM–BEM method, validity and efficiency of the new method and the corresponding numerical code are demonstrated. Eddy current perturbations and distributions of the magnetic flux density affected by a narrow crack of different directions are also numerically analyzed by using the new MME. In addition, the measurement results from the experiments give further support to the validity of the new MME method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently. [ABSTRACT FROM AUTHOR]